ozroaddeaths

ozroaddeaths is a package that pulls data from the Australian Road Deaths Database, run by the Bureau of Infrastructure, Transport and Regional Economics (BITRE). This provides basic details of road transport crash fatalities in Australia as reported by the police each month to the State and Territory road safety authorities. The details provided in the database fall into two groups:

1. The circumstances of the crash, for example, date, location, crash type

2. Some details regarding the persons killed, for example, age, gender and road user group.

Installation

You can install ozroaddeaths from github with:

# install.packages("devtools")
devtools::install_github("ropenscilabs/ozroaddeaths")

This data is taken from the Australian Road Deaths Database, which provides basic details of road transport crash fatalities in Australia as reported by the police each month to the State and Territory road safety authorities, obtained from: https://data.gov.au/dataset/ds-dga-5b530fb8-526e-4fbf-b0f6-aa24e84e4277/details?q=crash

Details provided in the database fall into two groups:

• the circumstances of the crash, for example, date, location, crash type

• some details regarding the persons killed, for example, age, gender and road user group.

The fatality data is updated every month. The heavy vehicle flags (for articulated truck, heavy rigid truck and bus involvement) are only updated each quarter, and are current to within two months. Information for heavy rigid truck involvement in crashes earlier than 2004 is incomplete. There is no day of the month for the data, so we have imputed this as the first of the month.

Package Author’s Notes

Data was available at URL as at 13th December 2019. Data is imported into R and cleaned by removing redundant date columns and transforming into a tidy format.

Indemnity Statement:

The Bureau of Infrastructure, Transport and Regional Economics has taken due care in preparing this information. However, noting that data have been provided by third parties, the Commonwealth gives no warranty as to the accuracy, reliability, fitness for purpose, or otherwise of the information.

Copyright

© Commonwealth of Australia, 2017

This work is copyright and the data contained in this publication should not be reproduced or used in any form without acknowledgement.

Import data from the BITRE website into R

library(ozroaddeaths)
library(dplyr)
library(ggplot2)
library(lubridate)
library(ggridges)

crashes <- oz_road_fatal_crash() 
fatalities <- oz_road_fatalities()

Variables available

Crashes

knitr::kable(dplyr::as_data_frame(names(crashes)))
#> Warning: `as_data_frame()` is deprecated, use `as_tibble()` (but mind the new semantics).
#> This warning is displayed once per session.

value
crash_id
n_fatalities
month
year
weekday
time
state
crash_type
bus
heavy_rigid_truck
articulated_truck
speed_limit
date
date_time

knitr::kable(head(crashes))

crash_id	n_fatalities	month	year	weekday	time	state	crash_type	bus	heavy_rigid_truck	articulated_truck	speed_limit	date	date_time
20193047	1	10	2019	Thursday	11:00:00	Qld	Single	No	No	No	100	2019-10-01	2019-10-01 11:00:00
20192202	1	10	2019	Thursday	21:06:00	Vic	Pedestrian	NA	NA	NA	NA	2019-10-01	2019-10-01 21:06:00
20191179	1	10	2019	Wednesday	06:45:00	NSW	Pedestrian	No	No	No	50	2019-10-01	2019-10-01 06:45:00
20192073	1	10	2019	Wednesday	07:15:00	Vic	Single	NA	NA	NA	NA	2019-10-01	2019-10-01 07:15:00
20193053	1	10	2019	Tuesday	15:00:00	Qld	Multiple	No	Yes	No	100	2019-10-01	2019-10-01 15:00:00
20192029	1	10	2019	Tuesday	12:03:00	Vic	Multiple	NA	Yes	NA	100	2019-10-01	2019-10-01 12:03:00

Fatalities

knitr::kable(dplyr::as_data_frame(names(fatalities)))

value
crash_id
month
year
weekday
time
state
crash_type
bus
heavy_rigid_truck
articulated_truck
speed_limit
road_user
gender
age
date
date_time

knitr::kable(head(fatalities))

crash_id	month	year	weekday	time	state	crash_type	bus	heavy_rigid_truck	articulated_truck	speed_limit	road_user	gender	age	date	date_time
20193047	10	2019	Thursday	11:00:00	Qld	Single	No	No	No	100	Driver	Male	25	2019-10-01	2019-10-01 11:00:00
20192202	10	2019	Thursday	21:06:00	Vic	Pedestrian	NA	NA	NA	NA	Pedestrian	Female	64	2019-10-01	2019-10-01 21:06:00
20191179	10	2019	Wednesday	06:45:00	NSW	Pedestrian	No	No	No	50	Pedestrian	Female	81	2019-10-01	2019-10-01 06:45:00
20192073	10	2019	Wednesday	07:15:00	Vic	Single	NA	NA	NA	NA	Passenger	Male	25	2019-10-01	2019-10-01 07:15:00
20193053	10	2019	Tuesday	15:00:00	Qld	Multiple	No	Yes	No	100	Motorcycle rider	Male	35	2019-10-01	2019-10-01 15:00:00
20191220	10	2019	Tuesday	06:40:00	NSW	Multiple	Yes	No	No	80	Motorcycle rider	Male	28	2019-10-01	2019-10-01 06:40:00

Plot crashes by year

crash_plot <- ggplot(crashes,
                     aes(x = year,
                         fill = year)) +
  geom_line(stat = "count") +
  theme_minimal() +
  ggtitle("Annual number of fatal car accidents per year")

crash_plot

Plot crashes by year and state

crash_plot +
  scale_y_continuous(trans = "log2") +
  facet_wrap(~state) +
   ggtitle("Annual number of fatal car accidents per year and state",
           subtitle = "log2 scale" )

Fatalities by year

fatality_plot <- fatalities %>%
  mutate(year = lubridate::year(date_time)) %>%
  ggplot(aes(x =  year, 
             fill = year)) +
  geom_line(stat = "count") +
  theme_minimal() +
  ggtitle("Annual number of road fatalities")

fatality_plot

fatality_plot <- fatalities %>%
  filter(gender != "Unspecified") %>%
  mutate(year = lubridate::year(date_time)) %>%
  ggplot(aes(x = age, 
             fill = gender )) +
  geom_density() +
  facet_wrap(~gender) +
  theme_minimal() +
  ggtitle("Distribution of road fatalities by age 1989 to 2017")

fatality_plot
#> Warning: Removed 82 rows containing non-finite values (stat_density).